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Homophily and minority-group size explain perception biases in social networks

Author

Listed:
  • Eun Lee

    (University of North Carolina at Chapel Hill)

  • Fariba Karimi

    (GESIS
    University of Koblenz-Landau)

  • Claudia Wagner

    (GESIS
    University of Koblenz-Landau)

  • Hang-Hyun Jo

    (Asia Pacific Center for Theoretical Physics
    Pohang University of Science and Technology
    Aalto University)

  • Markus Strohmaier

    (GESIS
    RWTH Aachen University)

  • Mirta Galesic

    (Santa Fe Institute
    Complexity Science Hub Vienna
    Max Planck Institute for Human Development)

Abstract

People’s perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show that these biases depend on the level of homophily, its asymmetric nature and on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations from six real-world networks. We also identify circumstances under which individuals can reduce their biases by relying on perceptions of their neighbours. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society.

Suggested Citation

  • Eun Lee & Fariba Karimi & Claudia Wagner & Hang-Hyun Jo & Markus Strohmaier & Mirta Galesic, 2019. "Homophily and minority-group size explain perception biases in social networks," Nature Human Behaviour, Nature, vol. 3(10), pages 1078-1087, October.
  • Handle: RePEc:nat:nathum:v:3:y:2019:i:10:d:10.1038_s41562-019-0677-4
    DOI: 10.1038/s41562-019-0677-4
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    Cited by:

    1. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    2. Hajibabaei, Anahita & Schiffauerova, Andrea & Ebadi, Ashkan, 2022. "Gender-specific patterns in the artificial intelligence scientific ecosystem," Journal of Informetrics, Elsevier, vol. 16(2).
    3. Raddant, Matthias & Takahashi, Hiroshi, 2022. "Interdependencies of female board member appointments," International Review of Financial Analysis, Elsevier, vol. 81(C).
    4. Andrew J. Stier & Sina Sajjadi & Fariba Karimi & Luís M. A. Bettencourt & Marc G. Berman, 2024. "Implicit racial biases are lower in more populous more diverse and less segregated US cities," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    5. Wang, Yukai & Yang, Zhongkai & Liu, Lanjian & Wang, Xianwen, 2020. "Gender bias in patenting process," Journal of Informetrics, Elsevier, vol. 14(3).
    6. You-Hai Lu & Peixue Liu & Xiaowan Zhang & Jun Zhang & Caiyun Shen, 2022. "Spatial-Temporal Differences in the Effect of Epidemic Risk Perception on Potential Travel Intention: A Macropsychology-Based Risk Perception Perspective," SAGE Open, , vol. 12(4), pages 21582440221, December.
    7. Nathan Berg & Jeong-Yoo Kim & Kyu Min Lee, 2021. "Why is parochialism prevalent?: an evolutionary approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(4), pages 769-796, October.
    8. Philipp Lorenz-Spreen & Stephan Lewandowsky & Cass R. Sunstein & Ralph Hertwig, 2020. "How behavioural sciences can promote truth, autonomy and democratic discourse online," Nature Human Behaviour, Nature, vol. 4(11), pages 1102-1109, November.
    9. Andreas Bjerre-Nielsen & Martin Benedikt Busch, 2022. "Statistical inference in social networks: how sampling bias and uncertainty shape decisions," Papers 2205.13046, arXiv.org.

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